{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "width = 6\n",
    "height = 3\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = [width, height]\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import math\n",
    "import pdb\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "import sklearn\n",
    "import sklearn.preprocessing\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from scipy.stats import pearsonr, spearmanr\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise:  Look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## we download historical data from 1990-2019\n",
    "## from Yahoo https://finance.yahoo.com/quote/%5EGSPC/history?period1=634885200&period2=1550034000&interval=1d&filter=history&frequency=1d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"sp500.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7306, 7)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990-02-13</td>\n",
       "      <td>330.079987</td>\n",
       "      <td>331.609985</td>\n",
       "      <td>327.920013</td>\n",
       "      <td>331.019989</td>\n",
       "      <td>331.019989</td>\n",
       "      <td>144490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990-02-14</td>\n",
       "      <td>331.019989</td>\n",
       "      <td>333.200012</td>\n",
       "      <td>330.640015</td>\n",
       "      <td>332.010010</td>\n",
       "      <td>332.010010</td>\n",
       "      <td>138530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990-02-15</td>\n",
       "      <td>332.010010</td>\n",
       "      <td>335.209991</td>\n",
       "      <td>331.609985</td>\n",
       "      <td>334.890015</td>\n",
       "      <td>334.890015</td>\n",
       "      <td>174620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990-02-16</td>\n",
       "      <td>334.890015</td>\n",
       "      <td>335.640015</td>\n",
       "      <td>332.420013</td>\n",
       "      <td>332.720001</td>\n",
       "      <td>332.720001</td>\n",
       "      <td>166840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990-02-20</td>\n",
       "      <td>332.720001</td>\n",
       "      <td>332.720001</td>\n",
       "      <td>326.260010</td>\n",
       "      <td>327.989990</td>\n",
       "      <td>327.989990</td>\n",
       "      <td>147300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  1990-02-13  330.079987  331.609985  327.920013  331.019989  331.019989   \n",
       "1  1990-02-14  331.019989  333.200012  330.640015  332.010010  332.010010   \n",
       "2  1990-02-15  332.010010  335.209991  331.609985  334.890015  334.890015   \n",
       "3  1990-02-16  334.890015  335.640015  332.420013  332.720001  332.720001   \n",
       "4  1990-02-20  332.720001  332.720001  326.260010  327.989990  327.989990   \n",
       "\n",
       "      Volume  \n",
       "0  144490000  \n",
       "1  138530000  \n",
       "2  174620000  \n",
       "3  166840000  \n",
       "4  147300000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7301</th>\n",
       "      <td>2019-02-06</td>\n",
       "      <td>2735.050049</td>\n",
       "      <td>2738.080078</td>\n",
       "      <td>2724.149902</td>\n",
       "      <td>2731.610107</td>\n",
       "      <td>2731.610107</td>\n",
       "      <td>3472690000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7302</th>\n",
       "      <td>2019-02-07</td>\n",
       "      <td>2717.530029</td>\n",
       "      <td>2719.320068</td>\n",
       "      <td>2687.260010</td>\n",
       "      <td>2706.050049</td>\n",
       "      <td>2706.050049</td>\n",
       "      <td>4099490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7303</th>\n",
       "      <td>2019-02-08</td>\n",
       "      <td>2692.360107</td>\n",
       "      <td>2708.070068</td>\n",
       "      <td>2681.830078</td>\n",
       "      <td>2707.879883</td>\n",
       "      <td>2707.879883</td>\n",
       "      <td>3622330000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7304</th>\n",
       "      <td>2019-02-11</td>\n",
       "      <td>2712.399902</td>\n",
       "      <td>2718.050049</td>\n",
       "      <td>2703.790039</td>\n",
       "      <td>2709.800049</td>\n",
       "      <td>2709.800049</td>\n",
       "      <td>3361970000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7305</th>\n",
       "      <td>2019-02-12</td>\n",
       "      <td>2722.610107</td>\n",
       "      <td>2748.189941</td>\n",
       "      <td>2722.610107</td>\n",
       "      <td>2744.729980</td>\n",
       "      <td>2744.729980</td>\n",
       "      <td>3827770000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date         Open         High          Low        Close  \\\n",
       "7301  2019-02-06  2735.050049  2738.080078  2724.149902  2731.610107   \n",
       "7302  2019-02-07  2717.530029  2719.320068  2687.260010  2706.050049   \n",
       "7303  2019-02-08  2692.360107  2708.070068  2681.830078  2707.879883   \n",
       "7304  2019-02-11  2712.399902  2718.050049  2703.790039  2709.800049   \n",
       "7305  2019-02-12  2722.610107  2748.189941  2722.610107  2744.729980   \n",
       "\n",
       "        Adj Close      Volume  \n",
       "7301  2731.610107  3472690000  \n",
       "7302  2706.050049  4099490000  \n",
       "7303  2707.879883  3622330000  \n",
       "7304  2709.800049  3361970000  \n",
       "7305  2744.729980  3827770000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## let's first take a look at our data\n",
    "df.index = df.Date\n",
    "fig = df.Close.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7eff46bf1ac8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(df.Close - df.Open).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## we can see there have been several \"regime changes\"\n",
    "## although it would be difficult to set an exact date of the change\n",
    "## but do different weeks look all that different?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7eff46b64e10>,\n",
       "  <matplotlib.axis.XTick at 0x7eff46b646d8>,\n",
       "  <matplotlib.axis.XTick at 0x7eff46b64080>,\n",
       "  <matplotlib.axis.XTick at 0x7eff46b8e470>,\n",
       "  <matplotlib.axis.XTick at 0x7eff46b8e978>],\n",
       " <a list of 5 Text xticklabel objects>)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vals = df[\"1990-05-05\":\"1990-05-11\"].Close.values\n",
    "mean_val = np.mean(vals)\n",
    "plt.plot([1, 2, 3, 4, 5], vals/mean_val)\n",
    "plt.xticks([1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7eff46b3fa58>,\n",
       "  <matplotlib.axis.XTick at 0x7eff46b3f320>,\n",
       "  <matplotlib.axis.XTick at 0x7eff46b3f208>,\n",
       "  <matplotlib.axis.XTick at 0x7eff46ae60b8>,\n",
       "  <matplotlib.axis.XTick at 0x7eff46ae6550>],\n",
       " <a list of 5 Text xticklabel objects>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vals = df[\"2000-05-05\":\"2000-05-11\"].Close.values\n",
    "mean_val = np.mean(vals)\n",
    "plt.plot([1, 2, 3, 4, 5], vals/mean_val)\n",
    "plt.xticks([1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "vals = df[\"2010-05-05\":\"2010-05-12\"].Close.values\n",
    "mean_val = np.mean(vals)\n",
    "plt.plot(vals/mean_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x7eff46b19d68>,\n",
       "  <matplotlib.axis.XTick at 0x7eff46b196a0>,\n",
       "  <matplotlib.axis.XTick at 0x7eff46b19400>,\n",
       "  <matplotlib.axis.XTick at 0x7eff46abe358>,\n",
       "  <matplotlib.axis.XTick at 0x7eff46abe860>],\n",
       " <a list of 5 Text xticklabel objects>)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5f+3n/O+3TmJEX82HLLFJRwgijWzqO2t5NreAW84+lkuH9Q67HJGDUiCINKLXln7KL177iNGDe3DruceFXY7IISkQRBrJksIyvvfsQk7qncb/fuskWukL6yTGKRBEGsHmsl1MmDGfzm2TeVzzIUucqFcgmNk0Mysys6UHWW9m9rCZrTazxWY2tNa68Wb2cXAZX2v5MDNbEuzzsOnTOdJMlFdUcf30XHZWVjPt2uGkt9d8yBIf6nuEMB0YdYj1FwD9gssk4DEAM+sE3AWMBEYAd5lZx2Cfx4Jt9+53qPsXiQt750Ne9dl2Hrl6KP27az5kiR/1CgR3fwcoOcQmY4CZHvEBkGZmPYDzgTfcvcTdtwJvAKOCdanu/r67OzATuPiIHolIDPifV1fw5kdF3P31gZyp+ZAlzkTrHEIvoKDW7cJg2aGWFx5g+ZeY2SQzyzWz3OLi4iiVKxJ9T3/wCU/+cx3XnZbF2FOywi5HpMGiFQgHGv/3w1j+5YXuU909x91z0tP1jkti0zurirnrpWWcPaArPx49MOxyRA5LtAKhEMiodbs3sKmO5b0PsFwk7nz82XZumrWAfl3baT5kiWvRCoSXgHFBt9HJQJm7bwbmAueZWcfgZPJ5wNxg3XYzOznoLhoH/CVKtYg0mZLySq6fMZ+U1glMu3Y47TQfssSxer16zWw2cBbQxcwKiXQOJQG4+xTgVeBCYDWwE7guWFdiZj8D5gd3dY+77z05/Z9EupeOAv4WXETiRmVVDf/5dB6fbavg2Uknaz5kiXv1CgR3v7KO9Q7cdJB104BpB1ieCwyqz+8XiTXuzl0vLeXf60r4zRXZDMnsWPdOIjFOn1QWOQx/+Nd6Zn9YwE1fPYYx2QdskBOJOwoEkQZ6e1Ux976ynPMGduP7X+sfdjkiUaNAEGmA1UU7uPmZBfTvnspDl2frC+ukWVEgiNRT6c5Kbpgxn+TEVjw+bhht1VEkzYxe0SL1sKe6hm/PWsCm0t3MnjSS3h3bhF2SSNQpEETq4Z6Xl/Pems958FsnMayPpsCU5klDRiJ1eOr99Tz1wSfc+JWjuUxTYEozpkAQOYR/rd7C3S8v55wBXfmvUQPCLkekUSkQRA5i3ZZyvj1rAcekt+XXV2TrO4qk2VMgiBxA2a49TJgxn4RWxpPjh9M+JSnskkQanQJBZD9V1TXc/MwCCkp28tjVQ8nopI4iaRnUZSSyn3tfWcG7H2/h/ktPZOTRncMuR6TJ6AhBpJZn/r2B6e+tZ8Lpfbl8eGbY5Yg0KQWCSOD9NZ/z//6ylDOPS+eOC9RRJC2PAkEE2PD5Tv5zVh59Orfht1cNITFBfxrS8uhVLy3e9t2RjiKAJ8cPJ1UdRdJCKRCkRauucb4zeyHrtpTz6NVDyerSNuySREKjLiNp0X7xtxW8tbKYey8exKnHdAm7HJFQ6QhBWqzncgt4/N11jD+lD9ec3CfsckRCp0CQFmn++hLu/NMSTj+2Cz+5aGDY5YjEBAWCtDgFJTu58ak8Mjq24ZGrhqqjSCSgvwRpUXZUVDFxZi5V1TU8MT6HDm3UUSSyl04qS4tRU+N8b04+HxftYPp1wzk6vV3YJYnEFB0hSIvxwOsr+fuKz/h/Fw3kjH7pYZcjEnMUCNIivLigkMfmreGqkZmMO0UdRSIHokCQZm/Bhq386IUlnHx0J376jRMw00Q3IgeiQJBmbWPpLibNzKNHWgqPXT2MJHUUiRyUTipLs7WzsoqJM3Kp2FPNnEkj6di2ddglicQ0BYI0SzU1zm3PLuKjT7fx5LXDObZr+7BLEol5On6WZunXf1/Fa8s+5b8vPJ6v9u8adjkicUGBIM3OS4s28fA/VnN5TgYTTu8bdjkicUOBIM1KfkEpP3x+ESOyOvGziwepo0ikARQI0mx8WrabSTNzSW+fzGPXDKV1ol7eIg2hvxhpFnZVVjNxZi7lFVU8OX44ndslh12SSNxRl5HEPXfnB39cxNJNZTw+Nof+3dVRJHI4dIQgce/hN1fzyuLN3D5qAOcO7BZ2OSJxS4Egce3VJZt56O+ruGRoL278ytFhlyMS1xQIEreWbizjtufyGZqZxn2XnKiOIpEjVK9AMLNRZrbSzFab2Y8OsL6Pmb1pZovNbJ6Z9a617n4zWxpcLq+1fLqZrTOz/OCSHZ2HJC1B0bbdTJyZS+e2yfx+bA7JiQlhlyQS9+oMBDNLAB4BLgAGAlea2f6T0D4IzHT3wcA9wH3BvqOBoUA2MBL4oZml1trvh+6eHVzyj/jRSIuwe081k57Ko3TnHh4fl0N6e3UUiURDfY4QRgCr3X2tu1cCc4Ax+20zEHgz+PmtWusHAm+7e5W7lwOLgFFHXra0VO7Oj15YTH5BKQ9dns3Anql17yQi9VKfQOgFFNS6XRgsq20RcGnw8zeB9mbWOVh+gZm1MbMuwFeBjFr7/TwYZnrIzPQ2T+r06Lw1/Dl/Ez88vz+jBnUPuxyRZqU+gXCgM3W+3+0fAGea2ULgTGAjUOXurwOvAu8Bs4H3gapgnzuAAcBwoBNw+wF/udkkM8s1s9zi4uJ6lCvN1dxln/LA3JWMye7Jt886JuxyRJqd+gRCIV98V98b2FR7A3ff5O6XuPsQ4M5gWVlw/fPgHMHXiITLx8HyzR5RAfyByNDUl7j7VHfPcfec9HTNg9tSLd+0jVufzeekjDTuv3SwOopEGkF9AmE+0M/M+ppZa+AK4KXaG5hZFzPbe193ANOC5QnB0BFmNhgYDLwe3O4RXBtwMbD0yB+ONEfF2yuYODOX1JQkHh87jJQkdRSJNIY6v7rC3avM7GZgLpAATHP3ZWZ2D5Dr7i8BZwH3mZkD7wA3BbsnAe8G7+a2Ade4+94ho1lmlk7kqCEfmBy9hyXNRUVVNZOfzuPz8gr+OPlUuqamhF2SSLNl7vufDohdOTk5npubG3YZ0kTcnR88v5gXFhTyyFVDGT24R9glicQlM8tz95y6ttMnlSVmPf7uWl5YUMj3zu2nMBBpAgoEiUlvrviM+/72EaMH9+C75/QLuxyRFkGBIDFn5afb+c7shQzq2YEHLztJHUUiTUSBIDGlpLySG2bOp21yIo+Py+Go1uooEmkqmiBHYkZlVQ2Tn86jaFsFz954Ct07qKNIpCkpECQmuDs/+fNSPlxXwm+uyCY7Iy3skkRaHA0ZSUyY9q/1PJtbwC1nH8uY7P2/KktEmoICQUI3b2URP39lOeef0I1bzz0u7HJEWiwFgoRqddF2bnlmIf27p/LQ5dm0aqWOIpGwKBAkNFvLK5kwI5fkpASeGJ9Dm9Y6pSUSJgWChGJPdQ03PbOAzaW7+f3YYfRKOyrskkRaPL0lk1D89OVlvLfmc371HycxrE/HsMsREXSEICGY+f56nv5gA5PPPIZLhvYOuxwRCSgQpEn98+Mt/PTl5Zx7fFd+eH7/sMsRkVoUCNJk1hbv4Nuz8ujXtR2/vmIICeooEokpCgRpEmU793DDjFwSE1rx+Lgc2iXr9JVIrFEgSKOrqq7h5tkLKNi6k9+PHUZGpzZhlyQiB6C3adLo7n1lBe9+vIVfXjqY4Vmdwi5HRA5CRwjSqGb9+xOmv7eeG07vy38Mzwi7HBE5BAWCNJr31mzhrr8s46z+6dxx4fFhlyMidVAgSKP45PNyvj1rAVld2vLwleooEokHCgSJum279zBhRi4AT47PITUlKeSKRKQ+FAgSVdU1zndmL2T9lnIeu3oYfTq3DbskEakndRlJVN336grmrSzmf755Iqcc0znsckSkAXSEIFHz7PwNPPHPdVx7ahZXjcwMuxwRaSAFgkTFh+tK+PGfl3JGvy78eLQ6ikTikQJBjlhByU4mP51HRsc2/O6qoSQm6GUlEo/0lytHZEdFFTfMyKWquoYnxufQ4Sh1FInEK51UlsNWXeN8b85CVhfvYMZ1Izg6vV3YJYnIEdARghy2B+au5O8rirjr6wM5vV+XsMsRkSOkQJDD8kJeIVPeXsM1J2cy7pSssMsRkShQIEiD5X2ylTteXMKpx3Tmrq+fEHY5IhIlCgRpkI2lu7jxqVx6pqXw6NVDSVJHkUizoZPKUm/lQUdRRVUNcyYNJ61N67BLEpEo0ts7qZeaGue25/JZ+ek2fnvlEI7tqo4ikeZGgSD18qs3VjF32Wf8ePRAzurfNexyRKQRKBCkTn/J38jv3lrNFcMzuO60rLDLEZFGokCQQ8ovKOWHf1zMiL6duGfMIMw00Y1Ic9UiTiq/t2YLn++oJCUpgeTEVnVet9LsXgBsLtvFxJm5dEtNZso1w2idqPcPIs1ZvQLBzEYBvwESgCfc/Rf7re8DTAPSgRLgGncvDNbdD4wONv2Zuz8bLO8LzAE6AQuAse5eecSP6ACmvrOWeSuL6719UoKRkphAclIrkoPrlLqu9wuV5L3X9Qigvdex9KVwuyqrmTQzj12V1cy6YSSd2qqjSKS5qzMQzCwBeAT4GlAIzDezl9x9ea3NHgRmuvsMMzsbuA8Ya2ajgaFANpAMvG1mf3P3bcD9wEPuPsfMpgATgMei+eD2uv/SwWzbtYfde2qoqKred11RVcPuPfW7rth3XUNJeSUVe2rYXVX9pWv3w68zsZV9KSha7xcyKV+4rn8wfWl5YgIpQeAlJdgXhoLcnR88v4ilm8p4cnwOx3VrH4V/BRGJdfU5QhgBrHb3tQBmNgcYA9QOhIHArcHPbwF/rrX8bXevAqrMbBEwysyeB84Grgq2mwHcTSMFQrfUFLqlpjTGXX+Bu7On2vcFRO3w2RdCB1lee/3/BUwkiPZel+3aQ9F+AbU3sKpqDj+JWhlfCIiEVsbG0l3894UDOHtAtyg+QyISy+oTCL2Aglq3C4GR+22zCLiUyLDSN4H2ZtY5WH6Xmf0KaAN8lUiQdAZKg6DYe5+9DvdBxAozo3WiRcbaGz9/vqCquqZ+RzoHOOr5wjZBII3r0YeJZxzdtA9CREJVn0A40BnW/d+O/gD4nZldC7wDbASq3P11MxsOvAcUA+8DVfW8z8gvN5sETALIzNS0jAeTmBA5B9E2uUX0CYhII6jPWcxCIKPW7d7AptobuPsmd7/E3YcAdwbLyoLrn7t7trt/jUgQfAxsAdLMLPFg91nrvqe6e46756SnpzfgoYmISEPUJxDmA/3MrK+ZtQauAF6qvYGZdTGzvfd1B5GOI8wsIRg6wswGA4OB193diZxruCzYZzzwlyN9MCIicvjqDIRgnP9mYC6wAnjO3ZeZ2T1m9o1gs7OAlWa2CugG/DxYngS8a2bLgalE2lH3nje4HbjNzFYTOafwZJQek4iIHAbzI+mTbGI5OTmem5sbdhkiInHFzPLcPaeu7WLnk1AiIhIqBYKIiAAKBBERCSgQREQEiLOTymZWDHxymLt3IfL5B6kfPV8No+erYfR8NcyRPl993L3OD3LFVSAcCTPLrc9ZdonQ89Uwer4aRs9XwzTV86UhIxERARQIIiISaEmBMDXsAuKMnq+G0fPVMHq+GqZJnq8Wcw5BREQOrSUdIYiIyCE0+0Aws2lmVmRmS8OuJR6YWYaZvWVmK8xsmZl9N+yaYpmZpZjZh2a2KHi+fhp2TfEg+CbkhWb217BriXVmtt7MlphZvpk16pe5NfshIzP7CrCDyJzPg8KuJ9aZWQ+gh7svMLP2QB5w8X5zaEvAIpNRt3X3HWaWBPwT+K67fxByaTHNzG4DcoBUd78o7HpimZmtB3LcvdE/t9HsjxDc/R2gJOw64oW7b3b3BcHP24l85XncT2/aWDxiR3AzKbg073dZR8jMegOjgSfCrkW+qNkHghw+M8sChgD/DreS2BYMf+QDRcAb7q7n69B+DfwXUBN2IXHCgdfNLC+YUrjRKBDkgMysHfAC8D133xZ2PbHM3avdPZvIVLAjzExDkwdhZhcBRe6eF3YtceQ0dx8KXADcFAyDNwoFgnxJMBb+AjDL3V8Mu5544e6lwDxgVMilxLLTgG8E4+JzgLPN7OlwS4pt7r4puC4C/gSMaKzfpUCQLwhOkj4JrHD3X4VdT6wzs3QzSwt+Pgo4F/go3Kpil7vf4e693T2LyPzs/3D3a0IuK2aZWduguQMzawucBzRax2SzDwQzmw28D/StKSpBAAAAcUlEQVQ3s0IzmxB2TTHuNGAskXdu+cHlwrCLimE9gLfMbDEwn8g5BLVSSrR0A/5pZouAD4FX3P21xvplzb7tVERE6qfZHyGIiEj9KBBERARQIIiISECBICIigAJBREQCCgQREQEUCCIiElAgiIgIAP8fTUrlvvd6NuYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vals = df[\"2018-05-05\":\"2018-05-11\"].Close.values\n",
    "mean_val = np.mean(vals)\n",
    "plt.plot([1, 2, 3, 4, 5], vals/mean_val)\n",
    "plt.xticks([1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## if we evaluate in terms of percent change within the week \n",
    "## none of these weeks seem distinctly different at the week-based scale to the eye"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## We will use a deep learning approach, so we need to normalize our inputs to fall \n",
    "## within -1 to 1. we want to do so without letting information leak backwards from the future\n",
    "## so we need to have a rolling smoothing process rather than taking the global mean to normalize\n",
    "## these columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## we want to predict daily returns (imagine you choose only to buy at start of day \n",
    "## and sell at end of day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-02-13</th>\n",
       "      <td>1990-02-13</td>\n",
       "      <td>330.079987</td>\n",
       "      <td>331.609985</td>\n",
       "      <td>327.920013</td>\n",
       "      <td>331.019989</td>\n",
       "      <td>331.019989</td>\n",
       "      <td>144490000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-14</th>\n",
       "      <td>1990-02-14</td>\n",
       "      <td>331.019989</td>\n",
       "      <td>333.200012</td>\n",
       "      <td>330.640015</td>\n",
       "      <td>332.010010</td>\n",
       "      <td>332.010010</td>\n",
       "      <td>138530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-15</th>\n",
       "      <td>1990-02-15</td>\n",
       "      <td>332.010010</td>\n",
       "      <td>335.209991</td>\n",
       "      <td>331.609985</td>\n",
       "      <td>334.890015</td>\n",
       "      <td>334.890015</td>\n",
       "      <td>174620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-16</th>\n",
       "      <td>1990-02-16</td>\n",
       "      <td>334.890015</td>\n",
       "      <td>335.640015</td>\n",
       "      <td>332.420013</td>\n",
       "      <td>332.720001</td>\n",
       "      <td>332.720001</td>\n",
       "      <td>166840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-20</th>\n",
       "      <td>1990-02-20</td>\n",
       "      <td>332.720001</td>\n",
       "      <td>332.720001</td>\n",
       "      <td>326.260010</td>\n",
       "      <td>327.989990</td>\n",
       "      <td>327.989990</td>\n",
       "      <td>147300000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date        Open        High         Low       Close  \\\n",
       "Date                                                                     \n",
       "1990-02-13  1990-02-13  330.079987  331.609985  327.920013  331.019989   \n",
       "1990-02-14  1990-02-14  331.019989  333.200012  330.640015  332.010010   \n",
       "1990-02-15  1990-02-15  332.010010  335.209991  331.609985  334.890015   \n",
       "1990-02-16  1990-02-16  334.890015  335.640015  332.420013  332.720001   \n",
       "1990-02-20  1990-02-20  332.720001  332.720001  326.260010  327.989990   \n",
       "\n",
       "             Adj Close     Volume  \n",
       "Date                               \n",
       "1990-02-13  331.019989  144490000  \n",
       "1990-02-14  332.010010  138530000  \n",
       "1990-02-15  334.890015  174620000  \n",
       "1990-02-16  332.720001  166840000  \n",
       "1990-02-20  327.989990  147300000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Return'] = df.Close - df.Open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7eff46ad3f98>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Return.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['DailyVolatility'] = df.High - df.Low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7eff46b2ec88>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.DailyVolatility.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "## as our inputs we will use daily volatility, daily return, and daily volume\n",
    "## all should be scaled appropriately so we need to compute rolling means to scale these"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "## we will use an exponentially weighted moving average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "ewdf = df.ewm(halflife = 10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7eff46a15198>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ewdf.DailyVolatility.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "vewdf = df.ewm(halflife = 10).var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7eff37cb0f60>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## notice that we don't fit to the smoothed values we merely use them to \n",
    "((df.DailyVolatility - ewdf.DailyVolatility)/ vewdf.DailyVolatility**0.5 ).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ScaledVolatility'] = ((df.DailyVolatility - ewdf.DailyVolatility)/ vewdf.DailyVolatility**0.5 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Return</th>\n",
       "      <th>DailyVolatility</th>\n",
       "      <th>ScaledVolatility</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-02-13</th>\n",
       "      <td>1990-02-13</td>\n",
       "      <td>330.079987</td>\n",
       "      <td>331.609985</td>\n",
       "      <td>327.920013</td>\n",
       "      <td>331.019989</td>\n",
       "      <td>331.019989</td>\n",
       "      <td>144490000</td>\n",
       "      <td>0.940002</td>\n",
       "      <td>3.689972</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-14</th>\n",
       "      <td>1990-02-14</td>\n",
       "      <td>331.019989</td>\n",
       "      <td>333.200012</td>\n",
       "      <td>330.640015</td>\n",
       "      <td>332.010010</td>\n",
       "      <td>332.010010</td>\n",
       "      <td>138530000</td>\n",
       "      <td>0.990021</td>\n",
       "      <td>2.559997</td>\n",
       "      <td>-0.682610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-15</th>\n",
       "      <td>1990-02-15</td>\n",
       "      <td>332.010010</td>\n",
       "      <td>335.209991</td>\n",
       "      <td>331.609985</td>\n",
       "      <td>334.890015</td>\n",
       "      <td>334.890015</td>\n",
       "      <td>174620000</td>\n",
       "      <td>2.880005</td>\n",
       "      <td>3.600006</td>\n",
       "      <td>0.507880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-16</th>\n",
       "      <td>1990-02-16</td>\n",
       "      <td>334.890015</td>\n",
       "      <td>335.640015</td>\n",
       "      <td>332.420013</td>\n",
       "      <td>332.720001</td>\n",
       "      <td>332.720001</td>\n",
       "      <td>166840000</td>\n",
       "      <td>-2.170014</td>\n",
       "      <td>3.220002</td>\n",
       "      <td>-0.088831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-20</th>\n",
       "      <td>1990-02-20</td>\n",
       "      <td>332.720001</td>\n",
       "      <td>332.720001</td>\n",
       "      <td>326.260010</td>\n",
       "      <td>327.989990</td>\n",
       "      <td>327.989990</td>\n",
       "      <td>147300000</td>\n",
       "      <td>-4.730011</td>\n",
       "      <td>6.459991</td>\n",
       "      <td>1.577997</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date        Open        High         Low       Close  \\\n",
       "Date                                                                     \n",
       "1990-02-13  1990-02-13  330.079987  331.609985  327.920013  331.019989   \n",
       "1990-02-14  1990-02-14  331.019989  333.200012  330.640015  332.010010   \n",
       "1990-02-15  1990-02-15  332.010010  335.209991  331.609985  334.890015   \n",
       "1990-02-16  1990-02-16  334.890015  335.640015  332.420013  332.720001   \n",
       "1990-02-20  1990-02-20  332.720001  332.720001  326.260010  327.989990   \n",
       "\n",
       "             Adj Close     Volume    Return  DailyVolatility  ScaledVolatility  \n",
       "Date                                                                            \n",
       "1990-02-13  331.019989  144490000  0.940002         3.689972               NaN  \n",
       "1990-02-14  332.010010  138530000  0.990021         2.559997         -0.682610  \n",
       "1990-02-15  334.890015  174620000  2.880005         3.600006          0.507880  \n",
       "1990-02-16  332.720001  166840000 -2.170014         3.220002         -0.088831  \n",
       "1990-02-20  327.989990  147300000 -4.730011         6.459991          1.577997  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ScaledReturn'] = ((df.Return - ewdf.Return)/ vewdf.Return**0.5 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ScaledVolume'] = ((df.Volume - ewdf.Volume)/ vewdf.Volume**0.5 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
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       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Return</th>\n",
       "      <th>DailyVolatility</th>\n",
       "      <th>ScaledVolatility</th>\n",
       "      <th>ScaledReturn</th>\n",
       "      <th>ScaledVolume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-02-13</th>\n",
       "      <td>1990-02-13</td>\n",
       "      <td>330.079987</td>\n",
       "      <td>331.609985</td>\n",
       "      <td>327.920013</td>\n",
       "      <td>331.019989</td>\n",
       "      <td>331.019989</td>\n",
       "      <td>144490000</td>\n",
       "      <td>0.940002</td>\n",
       "      <td>3.689972</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-14</th>\n",
       "      <td>1990-02-14</td>\n",
       "      <td>331.019989</td>\n",
       "      <td>333.200012</td>\n",
       "      <td>330.640015</td>\n",
       "      <td>332.010010</td>\n",
       "      <td>332.010010</td>\n",
       "      <td>138530000</td>\n",
       "      <td>0.990021</td>\n",
       "      <td>2.559997</td>\n",
       "      <td>-0.682610</td>\n",
       "      <td>0.682610</td>\n",
       "      <td>-0.682610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-15</th>\n",
       "      <td>1990-02-15</td>\n",
       "      <td>332.010010</td>\n",
       "      <td>335.209991</td>\n",
       "      <td>331.609985</td>\n",
       "      <td>334.890015</td>\n",
       "      <td>334.890015</td>\n",
       "      <td>174620000</td>\n",
       "      <td>2.880005</td>\n",
       "      <td>3.600006</td>\n",
       "      <td>0.507880</td>\n",
       "      <td>1.095396</td>\n",
       "      <td>1.083513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-16</th>\n",
       "      <td>1990-02-16</td>\n",
       "      <td>334.890015</td>\n",
       "      <td>335.640015</td>\n",
       "      <td>332.420013</td>\n",
       "      <td>332.720001</td>\n",
       "      <td>332.720001</td>\n",
       "      <td>166840000</td>\n",
       "      <td>-2.170014</td>\n",
       "      <td>3.220002</td>\n",
       "      <td>-0.088831</td>\n",
       "      <td>-1.272816</td>\n",
       "      <td>0.567948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-20</th>\n",
       "      <td>1990-02-20</td>\n",
       "      <td>332.720001</td>\n",
       "      <td>332.720001</td>\n",
       "      <td>326.260010</td>\n",
       "      <td>327.989990</td>\n",
       "      <td>327.989990</td>\n",
       "      <td>147300000</td>\n",
       "      <td>-4.730011</td>\n",
       "      <td>6.459991</td>\n",
       "      <td>1.577997</td>\n",
       "      <td>-1.320389</td>\n",
       "      <td>-0.486045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-21</th>\n",
       "      <td>1990-02-21</td>\n",
       "      <td>327.910004</td>\n",
       "      <td>328.170013</td>\n",
       "      <td>324.470001</td>\n",
       "      <td>327.670013</td>\n",
       "      <td>327.670013</td>\n",
       "      <td>159240000</td>\n",
       "      <td>-0.239991</td>\n",
       "      <td>3.700012</td>\n",
       "      <td>-0.172130</td>\n",
       "      <td>0.112849</td>\n",
       "      <td>0.261347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-22</th>\n",
       "      <td>1990-02-22</td>\n",
       "      <td>327.670013</td>\n",
       "      <td>330.980011</td>\n",
       "      <td>325.700012</td>\n",
       "      <td>325.700012</td>\n",
       "      <td>325.700012</td>\n",
       "      <td>184320000</td>\n",
       "      <td>-1.970001</td>\n",
       "      <td>5.279999</td>\n",
       "      <td>0.820098</td>\n",
       "      <td>-0.465662</td>\n",
       "      <td>1.393322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-23</th>\n",
       "      <td>1990-02-23</td>\n",
       "      <td>325.700012</td>\n",
       "      <td>326.149994</td>\n",
       "      <td>322.100006</td>\n",
       "      <td>324.149994</td>\n",
       "      <td>324.149994</td>\n",
       "      <td>148490000</td>\n",
       "      <td>-1.550018</td>\n",
       "      <td>4.049988</td>\n",
       "      <td>-0.082992</td>\n",
       "      <td>-0.275037</td>\n",
       "      <td>-0.635063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-26</th>\n",
       "      <td>1990-02-26</td>\n",
       "      <td>324.160004</td>\n",
       "      <td>328.670013</td>\n",
       "      <td>323.980011</td>\n",
       "      <td>328.670013</td>\n",
       "      <td>328.670013</td>\n",
       "      <td>148900000</td>\n",
       "      <td>4.510009</td>\n",
       "      <td>4.690002</td>\n",
       "      <td>0.401505</td>\n",
       "      <td>1.583178</td>\n",
       "      <td>-0.551605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-27</th>\n",
       "      <td>1990-02-27</td>\n",
       "      <td>328.679993</td>\n",
       "      <td>331.940002</td>\n",
       "      <td>328.470001</td>\n",
       "      <td>330.260010</td>\n",
       "      <td>330.260010</td>\n",
       "      <td>152590000</td>\n",
       "      <td>1.580017</td>\n",
       "      <td>3.470001</td>\n",
       "      <td>-0.600702</td>\n",
       "      <td>0.533116</td>\n",
       "      <td>-0.288110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-02-28</th>\n",
       "      <td>1990-02-28</td>\n",
       "      <td>330.260010</td>\n",
       "      <td>333.480011</td>\n",
       "      <td>330.160004</td>\n",
       "      <td>331.890015</td>\n",
       "      <td>331.890015</td>\n",
       "      <td>184400000</td>\n",
       "      <td>1.630005</td>\n",
       "      <td>3.320007</td>\n",
       "      <td>-0.668144</td>\n",
       "      <td>0.507159</td>\n",
       "      <td>1.478883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-03-01</th>\n",
       "      <td>1990-03-01</td>\n",
       "      <td>331.890015</td>\n",
       "      <td>334.399994</td>\n",
       "      <td>331.079987</td>\n",
       "      <td>332.739990</td>\n",
       "      <td>332.739990</td>\n",
       "      <td>157930000</td>\n",
       "      <td>0.849975</td>\n",
       "      <td>3.320007</td>\n",
       "      <td>-0.612112</td>\n",
       "      <td>0.199263</td>\n",
       "      <td>-0.128564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date        Open        High         Low       Close  \\\n",
       "Date                                                                     \n",
       "1990-02-13  1990-02-13  330.079987  331.609985  327.920013  331.019989   \n",
       "1990-02-14  1990-02-14  331.019989  333.200012  330.640015  332.010010   \n",
       "1990-02-15  1990-02-15  332.010010  335.209991  331.609985  334.890015   \n",
       "1990-02-16  1990-02-16  334.890015  335.640015  332.420013  332.720001   \n",
       "1990-02-20  1990-02-20  332.720001  332.720001  326.260010  327.989990   \n",
       "1990-02-21  1990-02-21  327.910004  328.170013  324.470001  327.670013   \n",
       "1990-02-22  1990-02-22  327.670013  330.980011  325.700012  325.700012   \n",
       "1990-02-23  1990-02-23  325.700012  326.149994  322.100006  324.149994   \n",
       "1990-02-26  1990-02-26  324.160004  328.670013  323.980011  328.670013   \n",
       "1990-02-27  1990-02-27  328.679993  331.940002  328.470001  330.260010   \n",
       "1990-02-28  1990-02-28  330.260010  333.480011  330.160004  331.890015   \n",
       "1990-03-01  1990-03-01  331.890015  334.399994  331.079987  332.739990   \n",
       "\n",
       "             Adj Close     Volume    Return  DailyVolatility  \\\n",
       "Date                                                           \n",
       "1990-02-13  331.019989  144490000  0.940002         3.689972   \n",
       "1990-02-14  332.010010  138530000  0.990021         2.559997   \n",
       "1990-02-15  334.890015  174620000  2.880005         3.600006   \n",
       "1990-02-16  332.720001  166840000 -2.170014         3.220002   \n",
       "1990-02-20  327.989990  147300000 -4.730011         6.459991   \n",
       "1990-02-21  327.670013  159240000 -0.239991         3.700012   \n",
       "1990-02-22  325.700012  184320000 -1.970001         5.279999   \n",
       "1990-02-23  324.149994  148490000 -1.550018         4.049988   \n",
       "1990-02-26  328.670013  148900000  4.510009         4.690002   \n",
       "1990-02-27  330.260010  152590000  1.580017         3.470001   \n",
       "1990-02-28  331.890015  184400000  1.630005         3.320007   \n",
       "1990-03-01  332.739990  157930000  0.849975         3.320007   \n",
       "\n",
       "            ScaledVolatility  ScaledReturn  ScaledVolume  \n",
       "Date                                                      \n",
       "1990-02-13               NaN           NaN           NaN  \n",
       "1990-02-14         -0.682610      0.682610     -0.682610  \n",
       "1990-02-15          0.507880      1.095396      1.083513  \n",
       "1990-02-16         -0.088831     -1.272816      0.567948  \n",
       "1990-02-20          1.577997     -1.320389     -0.486045  \n",
       "1990-02-21         -0.172130      0.112849      0.261347  \n",
       "1990-02-22          0.820098     -0.465662      1.393322  \n",
       "1990-02-23         -0.082992     -0.275037     -0.635063  \n",
       "1990-02-26          0.401505      1.583178     -0.551605  \n",
       "1990-02-27         -0.600702      0.533116     -0.288110  \n",
       "1990-02-28         -0.668144      0.507159      1.478883  \n",
       "1990-03-01         -0.612112      0.199263     -0.128564  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "## remove first row, which has na\n",
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "## now we need to form input arrays and target arrays\n",
    "## let's try to predict just a day ahead and see how we do\n",
    "## predicting stock prices is notoriously difficult so we should not\n",
    "## get ahead of ourselves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = df[:7000]\n",
    "test_df = df[7000:]\n",
    "X = train_df[:(7000 - 10)][[\"ScaledVolatility\", \"ScaledReturn\", \"ScaledVolume\"]].values\n",
    "Y = train_df[10:][\"ScaledReturn\"].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "## however batches are usually in form TNC\n",
    "## time, num examples, channels\n",
    "## so we need to reshape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6990, 3)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.expand_dims(X, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6990, 1, 3)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise: reshape X into 'TNC' form with numpy operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.split(X, X.shape[0]/10, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.concatenate(X, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 699, 3)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.68261013,  1.09539587, -1.2728162 , -1.32038878,  0.1128491 ,\n",
       "       -0.46566243, -0.27503675,  1.5831776 ,  0.53311559,  0.50715858])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:, 0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.1992625 ,  0.8803464 , -0.88575025,  1.31202992, -0.62081656,\n",
       "        0.9699946 , -1.06775852,  0.06323974, -1.17051705,  0.21707769])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:, 1, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.33628914,  1.29029955,  0.35875567, -1.0627572 , -0.92827774,\n",
       "       -1.57810457,  0.55630126,  0.10533493,  1.35843311,  0.02266415])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:, 2, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.68261013],\n",
       "       [ 1.09539587],\n",
       "       [-1.2728162 ],\n",
       "       [-1.32038878],\n",
       "       [ 0.1128491 ],\n",
       "       [-0.46566243],\n",
       "       [-0.27503675],\n",
       "       [ 1.5831776 ],\n",
       "       [ 0.53311559],\n",
       "       [ 0.50715858],\n",
       "       [ 0.1992625 ],\n",
       "       [ 0.8803464 ],\n",
       "       [-0.88575025],\n",
       "       [ 1.31202992],\n",
       "       [-0.62081656],\n",
       "       [ 0.9699946 ],\n",
       "       [-1.06775852],\n",
       "       [ 0.06323974],\n",
       "       [-1.17051705],\n",
       "       [ 0.21707769],\n",
       "       [ 0.33628914],\n",
       "       [ 1.29029955],\n",
       "       [ 0.35875567],\n",
       "       [-1.0627572 ],\n",
       "       [-0.92827774],\n",
       "       [-1.57810457],\n",
       "       [ 0.55630126],\n",
       "       [ 0.10533493],\n",
       "       [ 1.35843311],\n",
       "       [ 0.02266415],\n",
       "       [-0.64458727]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[:(7000 - 10)][[\"ScaledReturn\"]].values[:31]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_test = Y[::10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.1992625 ,  0.33628914, -0.64458727])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_test[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 699, 3)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(699,)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "## notice that we only used each data point once\n",
    "## but actually each data point can belong to many series, occupying a different position in the series\n",
    "## say it could be the first point or the last point or a middle point in the time series\n",
    "## rather than explicitly expanding out, we will simply cut off a random number of points\n",
    "## at each end so that for each epoch through training, we'll have different series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the neural network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_HIDDEN    = 8\n",
    "NUM_LAYERS    = 1\n",
    "LEARNING_RATE = 1e-2\n",
    "EPOCHS        = 10\n",
    "BATCH_SIZE    = 64\n",
    "WINDOW_SIZE   = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xinp = tf.placeholder(dtype = tf.float32, shape = [WINDOW_SIZE, None, 3])\n",
    "Yinp = tf.placeholder(dtype = tf.float32, shape = [None])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "with tf.variable_scope(\"scope1\", reuse=tf.AUTO_REUSE):\n",
    "    #rnn_cell = tf.nn.rnn_cell.LSTMCell(NUM_HIDDEN, dtype = tf.float32)\n",
    "    #rnn_cell = tf.nn.rnn_cell.DropoutWrapper(rnn_cell, output_keep_prob=0.9)\n",
    "    #rnn_output, states = tf.nn.dynamic_rnn(rnn_cell, Xinp, dtype=tf.float32) \n",
    "    \n",
    "    ## tf.nn.rnn_cell.MultiRNNCell\n",
    "    cells = [tf.nn.rnn_cell.LSTMCell(num_units=NUM_HIDDEN) for n in range(NUM_LAYERS)]\n",
    "    stacked_rnn_cell = tf.nn.rnn_cell.MultiRNNCell(cells)\n",
    "    rnn_output, states = tf.nn.dynamic_rnn(stacked_rnn_cell, Xinp, dtype=tf.float32) \n",
    "    W = tf.get_variable(\"W_fc\", [NUM_HIDDEN, 1], initializer = tf.random_uniform_initializer(-.2, .2))\n",
    "    output = tf.squeeze(tf.matmul(rnn_output[-1, :, :], W))\n",
    "    ## notice we have no bias because we expect average zero return\n",
    "    loss = tf.nn.l2_loss(output - Yinp)\n",
    "    opt = tf.train.GradientDescentOptimizer(LEARNING_RATE)\n",
    "    ##opt = tf.train.AdamOptimizer(LEARNING_RATE)\n",
    "    train_step = opt.minimize(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "## need to loop through data and find a way to jitter data \n",
    "## then need to also compute validation loss\n",
    "## and need to record results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess = tf.Session()\n",
    "sess.run(tf.tables_initializer())\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0   Training loss: 3202.92   Testing loss 4.65:\n",
      "Epoch: 1   Training loss: 3198.72   Testing loss 4.61:\n",
      "Epoch: 2   Training loss: 3196.39   Testing loss 4.56:\n",
      "Epoch: 3   Training loss: 3194.44   Testing loss 4.51:\n",
      "Epoch: 4   Training loss: 3192.61   Testing loss 4.48:\n",
      "Epoch: 5   Training loss: 3190.75   Testing loss 4.45:\n",
      "Epoch: 6   Training loss: 3188.83   Testing loss 4.42:\n",
      "Epoch: 7   Training loss: 3186.87   Testing loss 4.40:\n",
      "Epoch: 8   Training loss: 3184.98   Testing loss 4.37:\n",
      "Epoch: 9   Training loss: 3183.21   Testing loss 4.35:\n"
     ]
    }
   ],
   "source": [
    "## for each epoch\n",
    "y_hat_dict = {}\n",
    "Y_dict = {}\n",
    "\n",
    "in_sample_Y_dict = {}\n",
    "in_sample_y_hat_dict = {}\n",
    "\n",
    "for ep in range(EPOCHS):\n",
    "    ## for each offset to create a new series of distinct time series \n",
    "    ## (re: overlapping issue we talked about previously)\n",
    "    epoch_training_loss = 0.0\n",
    "    for i in range(WINDOW_SIZE):\n",
    "        X = train_df[:(7000 - WINDOW_SIZE)][[\"ScaledVolatility\", \"ScaledReturn\", \"ScaledVolume\"]].values\n",
    "        Y = train_df[WINDOW_SIZE:][\"ScaledReturn\"].values\n",
    "\n",
    "        ## make it divisible by window size\n",
    "        num_to_unpack = math.floor(X.shape[0] / WINDOW_SIZE)\n",
    "        start_idx = X.shape[0] - num_to_unpack * WINDOW_SIZE\n",
    "        X = X[start_idx:] \n",
    "        Y = Y[start_idx:]  \n",
    "        \n",
    "        X = X[i:-(WINDOW_SIZE-i)]\n",
    "        Y = Y[i:-(WINDOW_SIZE-i)]                                \n",
    "        \n",
    "        X = np.expand_dims(X, axis = 1)\n",
    "        X = np.split(X, X.shape[0]/WINDOW_SIZE, axis = 0)\n",
    "        X = np.concatenate(X, axis = 1)\n",
    "        Y = Y[::WINDOW_SIZE]\n",
    "        ## TRAINING\n",
    "        ## now batch it and run a sess\n",
    "        for j in range(math.ceil(Y.shape[0] / BATCH_SIZE)):\n",
    "            ll = BATCH_SIZE * j\n",
    "            ul = BATCH_SIZE * (j + 1)\n",
    "            \n",
    "            if ul > X.shape[1]:\n",
    "                ul = X.shape[1] - 1\n",
    "                ll = X.shape[1]- BATCH_SIZE\n",
    "            \n",
    "            training_loss, _, y_hat = sess.run([loss, train_step, output],\n",
    "                                       feed_dict = {\n",
    "                                           Xinp: X[:, ll:ul, :], Yinp: Y[ll:ul]\n",
    "                                       })\n",
    "            epoch_training_loss += training_loss          \n",
    "            \n",
    "            in_sample_Y_dict[ep]     = Y[ll:ul] ## notice this will only net us the last part of data trained on\n",
    "            in_sample_y_hat_dict[ep] = y_hat\n",
    "            \n",
    "        ## TESTING\n",
    "        X = test_df[:(test_df.shape[0] - WINDOW_SIZE)][[\"ScaledVolatility\", \"ScaledReturn\", \"ScaledVolume\"]].values\n",
    "        Y = test_df[WINDOW_SIZE:][\"ScaledReturn\"].values\n",
    "        num_to_unpack = math.floor(X.shape[0] / WINDOW_SIZE)\n",
    "        start_idx = X.shape[0] - num_to_unpack * WINDOW_SIZE\n",
    "        X = X[start_idx:] ## better to throw away beginning than end of training period when must delete\n",
    "        Y = Y[start_idx:]                              \n",
    "        \n",
    "        X = np.expand_dims(X, axis = 1)\n",
    "        X = np.split(X, X.shape[0]/WINDOW_SIZE, axis = 0)\n",
    "        X = np.concatenate(X, axis = 1)\n",
    "        Y = Y[::WINDOW_SIZE]\n",
    "        testing_loss, y_hat = sess.run([loss, output],\n",
    "                                 feed_dict = { Xinp: X, Yinp: Y })\n",
    "        ## nb this is not great. we should really have a validation loss apart from testing\n",
    "        \n",
    "    print(\"Epoch: %d   Training loss: %0.2f   Testing loss %0.2f:\" % (ep, epoch_training_loss, testing_loss))\n",
    "    Y_dict[ep] = Y\n",
    "    y_hat_dict[ep] = y_hat\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Y_dict[EPOCHS - 1])\n",
    "plt.plot(y_hat_dict[EPOCHS - 1], 'r')\n",
    "plt.title('Out of sample performance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(in_sample_Y_dict[EPOCHS - 1])\n",
    "plt.plot(in_sample_y_hat_dict[EPOCHS - 1], 'r')\n",
    "plt.title('In sample performance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4510042465998294, 0.1055373665178873)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearsonr(Y_dict[EPOCHS - 1], y_hat_dict[EPOCHS - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.15432340876554151, 0.227191315279568)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearsonr(in_sample_Y_dict[EPOCHS - 1], in_sample_y_hat_dict[EPOCHS - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.14725274725274726, pvalue=0.6154187624847252)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spearmanr(Y_dict[EPOCHS - 1], y_hat_dict[EPOCHS - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.09250192012288787, pvalue=0.47087456001778494)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spearmanr(in_sample_Y_dict[EPOCHS - 1], in_sample_y_hat_dict[EPOCHS - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ugNZWXTxmjKCUoqiqkbyMWIKDhHNnpvFO0SGa+4uMMhgMAYGrooAuU0qlK6VClVLjlVKPK6X+ppT6m7VeKaV+qJSaopSaqZTa7Ip2h0pPcZh+ZwQft3EOXHMNPPIIFBe7uWeeobKhjSNHO5meHgvAslkZtHc5eKdobI1yDAbD8BjTM4F7yBkoJ5AzfvtbCA+Hm25yY688R5HlAM6zBKAgM4G02Ahe2WaigQyGQCYgBCAjLoKY8JChC4DdrmcGv/iiriHs5/QIwDRLAIKChHNnprO6uIaGVlMa02AIVAJCAER0JNCugeYC9OXnP9dC8Mtf+n2KiMLKRjKToogJD/ls2bLZ6XR0O3ir0JiBDIZAJSAEALQjuLi6CTXUi3lMjJ4TsGYNvPKKW/vmbooqm5ieFnvcshMnxDMuPtJEAxkMAUzACECO3cbho53UNg8hEqiH735XO4X9OEVES3sXe+tayMs4XgBEhGWz01lbUsvhlmGcE4PBMGYIGAEYsDhMf4SG6hQRRUXwj3+4qWfu5dOqJpTiswig3pw/K4Muh+LNnaYspsEQiASMAGTbYwCG5wcA+NKX4OSTdWRQS4sbeoaed7B2rZ6A9p3vQGmpyw7dkwJierrtc+vyM2LJSooyuYEMhgAlZPBNxgYpMeEkRIUObwQAx1JELFoE990HN988uo4oBWVlsG6djjBatw62bTvexBQeDn/72+jasSiqbCQ2IoRx8ZGfWyciLJuVwX+/v5va5naSY8Jd0qbBYPAPAmYEICJDTwnRl1NO0SOBu+6C4WYobW6G997TM4wvvFAXn5kyBS6/XJuVbDYdcrp8uc5DdMUV8K9/uSwhXWFFI9PTYxFxlo9PRwM5FLyxw5iBDIZAI2BGAKD9AC99dBClVL8XxH654w6YMQN+/3t44AHn2ygFJSXH391/8gk4HHp9Tg6ccw4sWKAfM2boVNS9ufpqeOopeO457YQeBd0Oxa6qJi6dN6HfbXLtNqamxvDqtgq+uSBzVO0ZDAb/IqAEINtuo7m9i4qGNqcmkQGZNg2uugoefhh+/GOYOhUaGmDjxmMX+w0boN6qixMbC/Pnw69/rS/28+dDUtLg7Zx8MkyfDo8+OmoB2FvXQmtnt1MHcA/aDJTOX94pobqxDXtsxKjaNBgM/kNACUDv4jDDFgDQjuB//lObgwAKC/Vdvwjk5cGXv6wzii5YoAUjOHj4bYjoXET/9V+wfTvMmjX8Y1j0TQHRH8tmZXD/2yW8tr2SKxdNGnF7BoPBvwgYHwBAjhUJNCI/AEB6uq4fXFMDEyfq1ytXwuHDsGMHPPaYvmvPzx/Zxb+Hb34TwsL0KGAUFFU2EmLVQxiIqakxTEuzmUlhBkOAEVACEB8Vhj02fPDqYAPxi1/oWgGvvw633AJnnglxca7rJGhT0UUXwdNP6xDREVJY0cjU1BjCQwYXo/NnZ/DR/iMcPDLy9gwGg38RUAIAwygO422uvhqOHIEXXhjxIYoqmwa0//dm2ax0AF4zowCDIWAISAEoqW6m2+HjCd5OP107mh95ZES717d0UNXY5nQCmDMyk6KZOS7OTAozGAKIgBOAXLuN9i4HB+qPersrAyOiRwFr1+pUFMPkmAN46OapZbPS2V7ewL46N814NhgMPkXACUBPcZhR+QE8xRVX6HkCjz027F0LK/pPAdEf51lmIDMKMBi8x766Fo/doAacAGSnWpFAw80J5A3sdh1y+tRT0N4+rF2LKhuxx4aTNIz0DuMTojhpYrwRAIPBizz8/h7OfWANXd0Ot7cVcAIQHR7C+IRIig+5JtWC27n6aqirg5dfHtZuhZWNQ3YA92bZrAyKKhvZU+Mn58dgGEMopVhdXMMpU5IJCXb/5TngBACs4jD+MAIAWLIEsrKGNSegvaub3YeaB50A5ozzZqUjAq+aesEGg8fZU9NCRUMbi3NSPNJeQApATpqNPTXNdHS5f4g1aoKC9OSyd9+F3buHtMvuQ810OdSIRgD22AjmZiWaSWEGgxdYU6KTTZ6aneyR9gJTAOwxdDkUe/0l2uU739Ezi4foDO5xAPetAjZUzp+VTsmh5uHXTjAYDKNidXENk5KjmZAY5ZH2XCIAInK2iOwSkd0icoOT9d8WkRoR2Wo9rnJFuyMlxz6C6mDeZNw4OO88ePJJ6OwcdPOiyiYiQoPISooeUXNnz0gnSDCjAIPBg7R3dbO+tJ7FHrr7BxcIgIgEAw8B5wB5wGUikudk0+eUUidYj+HHNbqQKSkxBImfRAL1cPXVOgXFEArUF1Y2kJsWS3DQMFNeW6TYwlk4JYlXt1eilI9PmDMYxghb9h2mtbObU7M9Y/8H14wA5gG7lVKlSqkO4FngQhcc121EhAaTlRztH3MBejj7bBg/ftCZwUopiiqbRuQA7s2yWRmU1baw0zInGQwG97KmpJaQIGHBlCGkjXcRrhCAccCBXu/LrWV9uUhEtovICyLSb4USEblGRDaLyOaa4VbfGgY5qTaKq/0o1DEkBK68Umcf3bu3380qGtpoaO0kbxgTwJxxdn4aIUHCK8YMZDB4hNXFNZyUmUBMuOey9LtCAJzZGfraDV4BspRSs4C3gaf6O5hS6hGlVIFSqiAlxX1DoZw0G/vqWmjr7HZbGy7nyiv18xNP9LtJ0SgdwD0kRIdxytRkXjNmIIPB7dQ2t7OzopHTPBT+2YMrBKAc6H1HPx447rZRKVWnlOqZyvooMMcF7Y6KXLsNh9Ihk35DZqY2BT3xxPFF5HtRaOUAyk0bnQCAzg1UfriVrQeOjPpYBoOhfz7YXQt4LvyzB1cIwCYgW0QmiUgYcCmwovcGIpLe6+0FwPCzm7mYUReH8RZXXw0HD8IbbzhdXVTZSFZSlEuGkUvz0wgLDjKpIQwGN7OquIaEqFDyM1xcW2QQRi0ASqku4EfAm+gL+/NKqZ0icpuIXGBt9mMR2Ski24AfA98ebbujJSs5mtBg8S9HMMCyZTpHUD8zg4tGmALCGXGRoSzO0WYgh6+nzzYY/BSlFGtKalmUnTLiyL2R4pJ5AEqp15VSOUqpKUqp261lv1FKrbBe36iUyldKzVZKfUEp9akr2h0NocFBTEmJocSfHMEAoaF6Ythrr+mRQC+a27vYW3d01BFAvVk2K4Oqxja27D/ssmMaDIZj7Kpuoqap3ePmHwjQmcA95Nht/jnb9aqrwOH4nDN4V1VPCmjXCcCSPDvhIUG8us1EAxkM7mB1sWfTP/QmwAUghoNHWmlqG3x2rU8xZQqccQY8/rgWAovRpoBwRkx4CF+clsrrO6p8v4qaweCHrCmpJTs1hvS4SI+3HeACoGPlS/wpEqiHq6+Gffvgrbc+W1RY2URcZCjpcREubWrZrAxqmtrZUFbn0uMaDIFOW2c3G8rqPZb9sy8BLQC5VnWwEn9zBIMuFJOcfJwzWNcAsCHiWkfSF6elEhUWbKKBDAYXs6Gsno4uh1fMPxDgAjAhIYqI0CB2VfnhCCA8XJeMXL4cqqvpdih2VTUOqwbwUIkMC+aM6Xb+s6PKI1WKDIZAYU1xDWEhQcyf5Ln0D70JaAEIChKyU23+Nxegh6uu0hPCnnySstoW2jodw6oBPByWzUqnvqWDD/cYM5DB4CrWlNQyLyuRyLBgr7Qf0AIAViSQvwrAtGlw6qnw2GMUHdSzdV3pAO7NaTkp2MJDTIpog8FFVDe2sau6yWvmHzACQG5aDDVN7Rxu6fB2V0bGNdfA7t00v/k2IUHCVKvovauJCA3mzDxtBvKLSmoGg49zLPzTOw5gMALgf8Vh+nLRRRAfz6SX/83U1BjCQ9w3lFw2O53Gtq7PytYZDIaRs6akluSYcLeZbYeCEQB/F4DISPjmNzlpy/vMjXZvZtNFU1OIiww10UAGwyhxOBRrd9eyODvZ5VF7wyHgBSA9LgJbeIj/+gGAI9+4grDuTs7b/o5b2wkLCeKsfDtvFVb7Vxptg8HH2FnRSH1LB6fmeM/+D0YAEBFy0vysOEwfdiRm8lFGLrNefx7cnLt/2awMmtu7eH+XMQMZDCNltWVGXTS1l/2/qQnefBNuugm+9z2P9CPgBQC0Gai4uslvC58UVjbwzOyziCotgQ8+cGtbJ09JIjE6zEQDGQyjYE1JDXPjhJRVb8H118O8eZCQoOt93H03FBVBt/tH2Z6rPebD5NhjeGZjJzVN7aTGujaNgicoqmzi43lnwtp/6JnBixa5ra2Q4CDOnpHGyx8d5GhHF1Fh5idkMAyJ2lpYs4bOd9/jN//7GtMPlekRe1gYzJ8PN94IixfDySdDdLRHumT+vejqYADF1c1+KQCFFY1MykyFb3wDnnwS7r9f3024ifNnZfDvDft599NDLJuV4bZ2DF7m4EF4/nmIjYUJE449YtwTajzmqKqC1ath1Sr92LkTgKCISA7bszlw3fVM/PI5+uIf6flEcGAEAND1gUHn5V7kxUkZI6Gts5s9Nc0syUvVCeL+9jf417/gRz9yW5vzJiWSYgvn1W2VRgDGIlVV8Kc/wcMPQ3v759fHx8P48ceLQs+jZ/kQLmj1LR2EhwQRPdrqdQ6Htp8fOQKHDx97bm3V4hUff/wjOhrcEXlTXn7sYr96NezapZdHR8Mpp8DXvw6nncYfq6L519ZKtv12KbgxbHsoGAEAkmPCSYoOo9gPawPsPtRMl0PpGgCzpsFJJ2kz0A9/6J4fORAcJJw3M51nNu6nub3LJeUnDT5AbS3cdRf89a/Q0QHf+pY2S4SGwoEDn3+Ul8PmzVDjJCAgKcm5MEyYQG18Kg+VtPL0x1UszUvjoW+cpNvrffHu79nZsoaG49KiD0pwMMTFfV4YnC1zttxm0/+tvXuPXexXrYLSUn382Fg9Q/+734XTToMTT9Tn0OK9e95nweQkt87ZGSrmn2uRbY/xy1DQniLwn1UBu+Ya+P73YdMm7VhyE8tmpfPkh3t5u7CaL504zm3tGDzA4cPw5z/DX/4CLS3alPib30B29rFtsrL637+tTYtBjyj0Fol9+3RgQn39Z5snA78FrotJoJMg1NVHkdbWgfsYEaHNmvHx+jktDaZPP/be2XNk5LGRQc+joeH49z2P4uJjr5sHiQgUgagofa4AEhO17f666/QFf9YsLTJOOFB/lNLaFr6xIHPgNjyEEQCLXLuNF7aUo5Ty6sSM4VJU2UhkaDCZSZbT6LLL4Gc/g0cecasAnDQxgfS4CF7dXmEEwF9paND+onvvhcZG+NrX4NZb9YV1OEREwNSp+uGExrZOnly5k5Vvbia+vprz4rs4J76LzrJ9vFdUzfyTJjNp6vj+L+Tx8boNT9HVdUwo+hOMxkb9eU87DfLzIWhoAZVrd9cCsNhHTM1GACxy0my0dHRz8Egr4xOivN2dIVNY0ci0dNuxYtKxsXDppfDss3DffXq46gaCLDPQU+v20tDaSVxk6KD7GHyE5mZ48EEdbnj4sK4t8bvf6TtXF3K0o4snP9zL31eV0tDayTkLT+RnZ+aQbQVddHU7uPP2t/libir3XnKCS9seFSEh2oSV5PoUzWtKakiPi3Bbzq7hYuYBWPhjSgilFEWVjZ+vAXz11Xp4+swzbm1/2ewMOrsVK3dWubUdACoqBh+aGwbm6FFt6pk8WU82OvlkbcN/+WWXXvzbu7r5xwdlLL7rfe76zy5OnBjPKz9axMOXz/ns4g86pPiMaXbe+fQQnQFQZ6Kr28HaklpO9XL6h94YAbDISbUigfyoOMzBI600tnUds//3MH8+zJypzUBuZPb4OCYkRvKKu3MD1dfrz/OLX7i3nbFKWxs88ICuJf2LX8AJJ8C6dfDqqzBnjsua6ex28OzG/Xzh7vf53SuFTEmJ5oXvL+TJ78xj5njnhYqW5ttpaO1kU1m90/Vjie0HG2hs6/Jq9s++GAGwiIsKJS02wq/KQxZV6r5+bgQgokcBW7bAxx+7rX0R4byZGXywu5Z6d6bTvuMOLQJr17qvjbFIR4cOC87Ohp/8BHJzdcTKypWwYIHLmul2KJZvPciZ967ihpc+ISU2gqe/O59nr1lAQVbigPsuzk4hIjSINz0xivQya4prEYFFU33D/g8uEgAROVtEdonIbhG5wcn6cBF5zlq/QUSyXNGuq8mBQF0HAAAgAElEQVRJ86/iMIUVjYjAtDQndv7LL9eOs141g93BslnpdDsU/9nhpj/w3r367jUqSk+PN2agwenshMcfh5wcuPZamDgR3nkH3ntPhye6CKX0937OX1bzk2e3EhEazKPfKuD/fnAyi4Zo5ogMC2ZxdgorC6v9NhXLUFldUsOscXEkRId5uyufMWoBEJFg4CHgHCAPuExE8vps9l3gsFJqKnAf8KfRtusOclJjKDnUTLfDP36IRZWNZCVFO59Ik5AAF1+sJ4X1hKu5gfyMWCYlR7svN9Att+gIi3vv1bHeH33knnbGAt3d8M9/6iieq66ClBR44w09cvriF102L0QpxariGi586AO+//QWuroVD152Iq//+FTOzLMP2769ND+NyoY2dhxsdEn/fJGG1k62HjjiU+YfcM0IYB6wWylVqpTqAJ4FLuyzzYXAU9brF4AzxFe8IL3ISbPR0eVgX537LpiupLCyceBiEldfrcPVnn/ebX0QEZbNSmd9aR01TU5mjY6Gjz/WAvbTn+pIFdBOS8PxOBw66is/X0/eiomB5cth40adXMyFf7WNZfVc8vf1XPHERuqaO7jrq7NY+V+LOX92BkFBI2vnjGmpBAfJmDYDrdtTR7dDsThn7AnAOOBAr/fl1jKn2yiluoAGwGmMlYhcIyKbRWRzjbMZhm6kd04gX6eprZP99Uc/7wDuzaJFum6w281AGTgUvLHDxc7gX/1Kj2R+9Suw2/VM0k2bXNuGv7N2Lcyered/hITACy/oUdIFF7j0wr/twBG++fgGvvb3dZTVtXDbhfm8+4vT+FrBBEKCR3cZSYgOY15WIisLx64ArC6pITosmBMnxnu7K8fhCgFw9ivra0MZyjZ6oVKPKKUKlFIFKSmeVctsu47N9YdQ0F1V/TiAe9PjDF63DnbscFtfctNs5NhjeHWbCwVg5Up46y1tAoq3/jRz5xoB6M3evXD++drE9+9/w7ZtukToECclDUZPmPE1/7OZCx/6gB0HG7jp3Gmsvv4LfGthlktTGSzNt1Nc3UxZrX+MvoeDUorVxTUsnJJM6CjF0tW4YiJYOTCh1/vxQF+DcM825SISAsQBPhf3FRUWwoTESL9wBH+WAiJjAAGAY/lcHn1UT/V3E8tmZXDf28X85NmPmTkujlnj48nPiB1Zoi+HA375S51+4Nprjy2fOxdeeklHBCUOHF0y5uns1BP+HA4tlFOmDHnXts5uapraOdTUTk1TOzXN7dQ0tunnpl6P5nY6uxW28BD+a0kOVy7Kwhbhngl/Z+bZ+d0rhazcWcX3Thv6Z/EH9tUdpfxwK9csnuztrnwOVwjAJiBbRCYBB4FLga/32WYFcAWwDvgq8K7yUZd/rt3mF0nhiiobibdCVwckORm+/GXtHPzTn9w2pf6bCzIpOdTMprJ6lm/V+i8CU1NimDk+jlnj4phpiUJE6CB3jv/6l76b/fe/ITz82PK5c/Xz5s2wdKlbPoffcNNNsGGD9u9MmUK3Q1Hf0nHsgm5dxA81tR13Qa9paqepretzhxOBpOhwUm3hpNjCybbbSLGFkxEXwfmzM4iPcm/kyviEKGaMi2VlYfWYE4A1VvWvxT7mAAYXCIBSqktEfgS8CQQDTyildorIbcBmpdQK4HHgnyKyG33nf+lo23UXOXYb7++qoaPLQViIbw3XelNY0UheeuzQIi6uvhqeew5efFEn+nIDCdFhPHjZiQAcampjx8EGtpc38El5A6uLa3npo4OAziSanRrD7PHxWhjGx5GbZjtmTmhrg5tv1hOULrnk+EZ6Ji1t2hTYAvDaa3DPPRy89Nvc1jaJj29/m7qWDqfRazHhIaTYwkmJCWd6WiyLs/UFvufRc8FPjAobtS1/tCzNS+O+t4s51NRGqs3/6nL0x6riWiYkRpKZ5HspZlySC0gp9Trwep9lv+n1ug242BVtuZvcNBtdDkVZbQu5zuLrfYCubgefVjVx+VAzCn7hC9pE8OijbhOA3qTaIvjitAi+OM0OaBtodWM728uP8IklDCsLq3hus44dCA0WpqXFMnN8HBe/9ywn7t9P12OPE9LXlh0fr2PbAzgSqPKTYuIvu5wD6VM4f9wFxOw9zOm5qaTHRRx3Qe95+FPFtqX5du59q5h3ig5x2byJ3u6OS+jsdrBuTy0XnjjOZ9I/9MZ/fh0eIjv1WHEYXxWAvXUttHc5BnYA9yYoSMeF33ijLlKRm+veDvZBREiLiyAtLo2l+WmAFoWDR1rZXm6NFA4eYdX6Xfzqkb/w3uQ5fG9VJ3nFHzBrfNxnPoXs1BiCCgp07vUAorPbwduF1fzvujKu/f3V2NrbefRXd3DfBQtYMt3u0yPV4ZBrtzExMYo3d1aNGQH4eP8RWjq6fdL8A0YAPsfklGiCg0T7AWZ7uzfOKbRSQAwYAtqXb39bR9Q8+ijcc497OjYMRITxCVGMT4ji3JnpAKhf/AI6jiJ33skVURlsL2/gxS3l/M+6fQB877TJ3Dh3rvYNVFZCero3P4LbKatt4dlN+3lxSzm1zR38bt3TzC0vpO7vT3D3NX4xoB4WIsJZ+Xae+nAfTW2dbnM4e5LVxTUEBwkLp7g+s6grMALQh4jQYLKSonw6FLSwopHQYBleStm0ND0z+MEHdd53N9YKGBH79iEPPghXXMHpFy/hdGuxw6EorW3h+he2sWpXjRYA0H6ACy7wVm/dRltnN//ZUcUzG/ezoaye4CDhi9NS+WHHbk7407Nw1VUkXfMdb3fTbSzNT+PRNWWsKq4ZE+VG15TUcMKEeJ9Nl24EwAm5aTYKK3x3WnpRZSNTU23DH/o/+CB8+KEWgi1bdISQr9CT8uG2245bHBSkhe70nFTuf6eYhmmLiAsOHnMC8GlVI89uPMDLHx+kobWTiYlRXH9WLl+dMx57cz2ccC7MmOHWUF5f4KSJCSRFh/Hmzmq/F4DDLR1sP9jAT87IHnxjL2EEwAnZqTbe2FFFa0c3kWHer9vZl8LKRk4dSUWhpCQdCXTyydoZ/Prr/Zau8yhbt8LTT+vY/wkTnG4yNysBpeDj2nZOz88fExPCWtq7eGVbBc9uOsDWA0cICw7irBlpXDp3AgsnJ+nUCl1dcMHX9WSv55/XSfHGMMFBwpl5dl7dXkl7V7dP1M0dKWt316IUPpf+oTdjw3vkYnLTbCilC677GrVWLPew7P+9mTNHF/1eufJzd9teoyflww2fSyT7GSdMjCc4SNi89/CxGcG+OZVkQJRSbDtwhBtf2s6829/mhpc+obm9i5vPm876m87gwctO5JSpycfy6tx2m3Z6P/zw8Es1+ilL8+00t3exvtTn5ooOizUlNcRGhDBrnPNaCL6AGQE4oXd1sP4KWXiLor5F4EfCVVfp9BC33aaLx5x7rot6NwLeekuL0b33Hkv54ISosBDyM2LZtLdeC8Djj+tUCJMmea6vo6DhaCf/t/Ugz246QFFlIxGhQSyblcFl8yZw0sQE5yGCb78Nf/iDduB/61se77O3OHlKMlFhwazcWcVpPnz3PBBKKdaU1HLK1GSvz68YCCMATshKiiIsOMgnHcE9vokhh4A6QwQeekgnDbv8cu0P8MaFtHfKhx/8YNDNCzIT+ffGfXQuO4lQ0KMAHxcAh0Nx26uFPLNxP+1dDmaMi+UPX5rBBSdkEDtQlEtVlf5upk3TI7YAIiI0mNNzU3irsJrfXzhjxFlGvcnuQ81UNrTx4zN8W8B8V5q8SEhwEJNTon0yJ1BRZSPpcRGjLyoRGan9AQ4HfPWregaup/n3v7X9//bbj0/50A8FWQm0dTrYmTQRwsL8wg9QWNnIkx/uZcl0O69et4hXrzuVyxdkDnzx7+7WPpqeVN7R0Z7rsI9wVn4ah5ra2Vp+xNtdGRGrS2oB36r+5QwjAP2Qm2ajxAfTQhdWNo7O/NObKVN0jqCPPoLrrnPNMYdKWxv8+tdw0kk6qdkQKMhMAGBzZYuua+sHArC+tA6Am5dNZ8ZQbcF//CO8+66O2poxw429811Oz00lJEhYubPa210ZEWtKapicHM2ERN922hsB6Iccu42DR1ppauv0dlc+o62zmz01LaMz//Tl/PN1YrHHHoMnnnDdcQfjoYdg/364++4hpy9OjY1gYmLUMT/Ali36btmHWV9aT2ZSFOlxkUPbYdUquPVWPQK48kq39s2XiYsMZeGUJFburPK7UpHtXd2sL63z6eifHowA9EOODxaHKanW5SpdKgCgncFnnAE//KFbi8h/xuHD2uxz9tm6VOEwKMhKYMu+w6iCAl0feNcuN3Vy9Dgcik1761kwaYizQA8d0oVdpk7VUT8+mDvGkyzNT6O0toU9Nb7zHxwKm/cepq3TMbJQbQ9jBKAfcntFAvkKRUOtATBcgoPhmWf0xLCLLtIXaHdyxx1w5IhOTz1MCjITqW3u4GC2ZRrx4cRwRVWNNLR2smDKEGoXOBzwzW/qWgfPPw8238xD5UnOnK6TCb7pZ2ag1SU1hAYLCyb7ZvqH3hgB6IfxCZFEhgb7lAAUVjYSFRZMpjvsiikp8L//C+Xl+kLkcLi+DYB9++CBB3RY46xZw959bpb2A6wLSda1b33YD9ATxz5/KCOAP/1Jh8Pef78u8WggLS6C2RPiWVnoXwKwpriWkyYmjKwYkocxAtAPQUFCjj3G5wRgWprNfWFxCxbAfffpfPN33OGeNm65RT///vcj2n1KSgzxUaFsPtCoHcg+LAAbSuuYmBhFRvwg9v+1a/V5+drX4Hvf80zn/ISleXa2HThCVYMXotRGQE1TO4WVjX5h/wcjAAOSbbexq8o37I899Vldbv/vyw9+oB2Qt9yiJ2m5kp6UDz/5Sb8pHwYjKEiYMzGBTfssR/DWrdDR4dp+ugCHQ7GhrJ4Fkwcx/9TVabt/VpbO1Brgdv++nGWlD3/LTwrGr93tu9W/nGEEYABy7TZqm9upb/H+Bab8cCtNbV2ut//3RQT+/nfIz9cXpv37XXfsnpQPN944qsMUZCVSWtNC08wToL3drQXvR8qnVU00tHYObP5xOOCKK7Tz9/nnIdbN360fMjU1hskp0X5jBlpTXEtidBj57v6fuggjAAOQk+Y7juAeB7DbRwCgJx69+KK+s774Yn2RHS09KR9uvnnAlA9DocDyA2y1W1kWfdAMtKFMx//PH2gEcO+92tz25z9rc5bBKUvz0li3p46Go74Tku0Mh0OxuqSWRb1zOfk4RgAGwJcigQorGxGBaZ6qUpaTA08+CRs3ws9+NrpjDTPlw2DMHBdHWHAQa7ttOsOpDwrA+tI6JiRGMj6hH4f9+vV6JPSVr+jwW0O/LM230+VQvLfrkLe7MiCfVjVR29zuF+GfPRgBGAB7bDi2iBB2VXlfAIoqG5mUFO3ZGq9f+Qr84hfw3/+tbfcjZZgpHwYjIjSYmePj2LTvMBQU+FwoaI/9v1/zz+HDuuD9+PE6qZ2x+w/ICePjSbWFs9LH/QBrSrT9/1Q/sf+DEYABERFy7TafGQF4xPzTlzvugMWL4Zpr4JNPhr9/W5s2+wwj5cNQKMhK4JODDXSdeJL2ARw96rJjj5biQ00cOdrpPA5cKfjOd3RJy+eeG7U5LBAIsmoEvL+rhrZO3535vbqkhly7jbS4CG93ZcgYARiEnDQbxdXNXp2O3tjWyYH6Vvc7gJ0REnLsQnXRRdDQMLz9H3pIx/7fddeQUz4MhbmZiXR2K0on5el0EFu3uuzYo2X9Hsv+P8mJ/f+BB2D5ch3372tlOX2YpflpHO3o5oPdtd7uilNaO7rZVHbYr8w/YARgUHLtNhpaOznU5AJH6Aj51CoCPz3dS7ND09J0lEppqb57HaoY9qR8OOssnWrChcyxEsN9kGClg/YhP8CGsnrGxUd+PhHYpk1w/fW6lOVPf+qdzvkpCycnYQsP8dnkcBvK6ujodnCqn8T/9zAqARCRRBF5S0RKrOeEfrbrFpGt1mPFaNr0NNl2XXjdm36AY0VgvFicZtEinbjt5ZfhnnuGts8oUj4MRkJ0GFNTY1jTEgYZGT4jAMfi//uYf44c0Xb/tDT4xz+M3X+YhIUE8YVpqbxdVE23w/eSw60pqSUsJIh5WUNI++FDjHYEcAPwjlIqG3jHeu+MVqXUCdbDryp5+0IkUFFlIwlRodhjR+9AHRU//akOC73hBnj//YG33b//WMoHN6U2KMhMYPPeelTBXJ8RgJJDzdS3dBwf/qmUrsK2fz88+ywk+tdFwldYmm+nrqWDLfvcnKtqBKwurmH+pESfrCE+EKMVgAuBp6zXTwFfGuXxfI6kmHCSY8K8KgCFlY3kZcQ6LxvoSUR01EpOjnboVlT0v21Pygc31h0uyEqksa2L2umzoLh4+P4JN9AT/7+w9wjg4Yf1vIo//hFOPtlLPfN/TstJISw4iJU7fSsaqLKhlZJDzX5n/4fRC4BdKVUJYD2n9rNdhIhsFpH1IjKgSIjINda2m2tqakbZPdeQY7exy0tpobu6HeyqamJ6mo/MLLTZ9MWsuVnnrul0Mjln2zZdaOYnP4GJE93WlZ7EcNszrAlhW7a4ra2hsr60jnHxkYxPsPL/1NbqeRTnnKNDag0jxhYRyilTk1hZWO1TNQLWWNW//Cn8s4dBBUBE3haRHU4eFw6jnYlKqQLg68D9IjKlvw2VUo8opQqUUgUpKb5xQnPsNkqqm3B4wfZYVttCe5fDOxFA/ZGXp0cCH3ygJ3j15Ve/0lFDN/RnEXQNExOjSI4J590oK6+Ql81ASik2lNYzf1LisdHaa6/pmdS//71Lo6AClaX5aeyvP8qnPjA3p4fVxTWk2MI9N0nThQz6i1RKLVFKzXDyWA5Ui0g6gPXsdKqeUqrCei4F3gdOdNkn8AA5dhtHO7o5eKTV420XejIFxHC45BJ9h3///TpCqIe334Y339Sx/wlOYwJchogwNyuB1fUOmDzZ6wKw+1AzdS0dxzuAly+HceNMqgcXsWS6HRF8Jhqo26FYu7uWU7OTvW+iHQGjvSVZAVxhvb4CWN53AxFJEJFw63UycApQOMp2PUpumo4E8oYfoLCykbDgIKakxHi87UG56y5t077ySigqOj7lg4fSG8zJTOBAfSttJ3g/NXRP/d/PBKCtTYvhBReYqB8XkWILZ87EBJ+ZFbyzooEjRzv9JvtnX0YrAHcCZ4pICXCm9R4RKRCRx6xtpgObRWQb8B5wp1LKrwQg24oE2uUFASiqbGJqagxhIT5oPggL03f/0dE6bcQjj+iSkn/4g0tSPgyFuVbYXWlWno6yOeS9fDHrS+tJj4tgQqJl/3/nHT1D+cLhWEsNg7E0387OikbKD3t/9vfqYu2nXOSHDmAYpQAopeqUUmcopbKt53pr+Wal1FXW6w+VUjOVUrOt58dd0XFPEhsRSnpcBMVesDsWVngpBcRQGTdOhzYWF8O118KJJ+o00h4iLyOWyNBgNiRP1gu8NApQSrGhrI4Fk5OOmQJWrNBO89NP90qfxipn5ukaAb5gBlpdUkt+RizJMV4O0R4hPnhb6Zvk2G0eLxBf09RObXO7bzmAnfGFL8Cdd2on5913e9TZGRocxAkT4nktNE2366XEcHtqmqlt7jhWAMbh0AJw9tkeGw0FCpOSo8mxx3jdDNTc3sVH+w77ZfRPD0YAhkhumo3dNc10dbupVq4TjtUA8IPoguuvh5oal6d8GApzsxL4qK6L7mnTvTYC+Fz9302boKrKmH/cxFn5aWwsq+ewF4s1rdtTR5dDsdhPzT9gBGDI5NhtdHQ52FfvObtj4WcpIHx8BNCDl2a4zslKxKGgNneGvvB6IUZ8fWkdabERZCZZ+X9WrIDgYB3/b3A5S/PScCh4u8h7ZqA1JTVEhgYzJ8u90W7uxAjAEMmxcgJ50g9QVNlIRlwE8VFhHmvTHzlpYjxBAjvH5Won8IEDHm1fKcX6Ul3/9zP7//LlOo22SfvgFmaMiyUjLsKrpSLXlNSyYHIi4SH+lf6hN0YAhsjU1BhE8KgfoLCi0fft/z6ALSKU3LRY3o/N1As8bAYqrW2htrmd+T3hn3v2wM6dOvzT4BZEhKX5aawpqaG1w/M1Ag7UH6WstsWv7f9gBGDIRIWFMDExymNzAdo6uymtbfHtCCAfYm5WAitUMio01OMC8Ln4/xVWwltj/3crS/PstHU6WF3i+ZQxPW0u9rP0z30xAjAMslNtHpsLUFzdRLdD+Y/938vMyUzgiCOYtun5Ho8EWl9aT6otnKze9v+ZM2HSJI/2I9CYOymRuMhQ3vRCcrg1xbVkxEUwJSXa4227EiMAwyA3LcbKzeP+IWeRr6aA8FF6JoTtm5ynBcDhmWgtnf+nV/x/fT2sWWPMPx4gNDiIM6al8k7RIY9H5+n0Dyl+mf6hN0YAhkGO3Ua3Q1FW2+L2tgorGokOC2Zi36pSBqdkxEcyLj6SzSlTdFro3bs90m5ZbQuHmtqPmX9ee02XqDTmH4+wND+NhtZONu6t90h7b+6s4qKHPyQ6PJirF/v/CM8IwDDItbL9eaI6WFFlE9PSYwkK8u87DE8yJzOBNyLG6zce8gN8Fv/fMwFsxQpIT4c5czzSfqCzOCeZ8JAgt88KVkrx0Hu7+d4/t5CdGsOKHy1iaqofzM8ZBCMAw2BScjTBQeJ2R7BSiqLKRv+YAOZDzM1KYH1EGo7ISI8JwIayOlJs4UxOjtZpn//zH23+MamfPUJUWAinZqewcmeV22oEtHV285Nnt3L3m7u48IQMnvveQuyxEW5py9OYX+kwCA8JZlJyNLuq3BsKWn64lab2Lu/WAPZD5mQm0h0UzOGeCWFuRsf/97L/v/eeLpRj7P8eZWm+nYqGNnZWNLr82NWNbVzy93Ws2FbB9Wflcv8lJxAR6r9x/30xAjBMcu02tpcfodANP7YeCv0pBYQPkZtmwxYewqfjc3VW0q4ut7a3t+4o1Y3tzJ9kmX+WL9eZUb/4Rbe2azieJdPtBAkujwbaXn6EC/66lpJDzTzyzTn88AtT/d7p2xcjAMPkwhMyONLaybkPrOHChz7guU37aWl37YWmsKKRIIFpvlIG0k8IDhJOzExgTVwWtLZCoXuzjm/oHf/fO/lbxNgwD/gLidFhzM1KdKkfYMW2Ci7+2zpCgoJ48dqTWZqf5rJj+xJGAIbJ0vw0Nt50Br9ZlsfR9i5+9eInzLv9bW586RM+KXdNUfKiykaykqOJDBs7Q01PMTczgf94qETk+tI6kmPCdSz4Rx9BRYUx/3iJpflp7KpuYu8oI/QcDsWfV+7ix898zKzxcaz40SljOhTbCMAIiI8K48pFk1j5X4t58dqFnDMznZc/Luf8v67lvAfW8PT6fTS1OSmWPkQKKxvNBLARUpCVyL6EdDptsW4VAJ3/v575Pfl/li/Xjt/zznNbm4b+WZpnB+CtUeQGamnv4tp/beHBd3fztYLx/OuqBST5aZ7/oWIEYBSICHMyE7nn4tlsuGkJv78wH4eCm/9vB/Nuf4dfvrCNj/YfHlZ0QkNrJ+WHW8f0XYc7OWFCPMHBwRyckudWAdhff5TKhrbj0z8sWgRJSQPvaHALExKjyEuPHbEfoPzwUb76t3W8VVjNLcvy+NNFs3yzCp+LCfF2B8YKcZGhfHNhFpcvyGR7eQPPbNzPim0VPL+5nGlpNi6dO4EvnzieuKjQAY/zqb+lgPYxIsOCyR8Xx8f2bLLeeUbX5XWDTb4n/8/CyYlQVgbbt8Of/+zydgxDZ2m+nb+8U0JNUzsptqHfuW/ZV8/3/rmF9i4H//jOPE7z8/w+w2HsS5yHERFmT4jnzotmsfHXS/jjl2cSFhLEra8UMu+Pb/Oz57aysay+31FBTwoIkwV05BRkJvBOzEQdBbRtm1va2FBaT3JMGFNSYuCVV/RCY//3Kkvz0lAK3hlGjYD/3XyAyx7ZQEx4CC//4JSAuviDGQG4lZjwEL4+fyJfnz+RHQcbeHbTfpZ/XMFLHx9kSko0l82byFdOGk9i9LF8/4WVjSRGh5E6jDsYw/HMzUrgd6lT9ZvNm2H+fJcevyf+f/6kpGP2/7w8mDrVpe0Yhsf0dBsTEiN5c2cVl86bOOC23Q7FnW8U8eiaMk6ZmsRDXz8pIOtumBGAh5gxLo4/fGkmG359Bnd9dRZxkaH84bUiFvzxHa575mM+3FNrzQBuIi89dszFG3uSOZmJVNqSOZqY7BY/wIH6Vioa2nT938OHYdUqk/vHBxARlual8cHuOpoHCM1ubOvkqqc28eiaMq5YmMmT35kXkBd/MCMAjxMVFsLXCibwtYIJ7Kpq4pmN+3npo3Je2VZBVlIUFUfauOLkTG93069JsYWTlRxNycRpzHaDAKwv0/b/+ZOT4I1XdPI3Y/7xCZbm2Xl8bRmrdtVw3qz0z63fV9fCd5/azN7aFm7/8gy+MT+w/2tmBOBFctNs3HpBPht/vYT7LplNqi2Cjm4H8yaZSJLRUpCVyLqESaiiImhybe6m9aV1JEaHkZ0ao80/djvMm+fSNgwjoyArkcToMFYWfj4a6MM9tVz40AfUNrfzz+/OD/iLP4xSAETkYhHZKSIOESkYYLuzRWSXiOwWkRtG0+ZYJCI0mC+fOJ7nv7+Q7bcuZcn0VG93ye8pyExgfdJkRCk9ScuFbCitZ/6kRKSzE954A84/3yR/8xGCg4Ql01N599NDdHQdqxHw9Pp9fOvxjaTEhLP8h6ewcIq5yYLRjwB2AF8BVve3gYgEAw8B5wB5wGUikjfKdscssRGhxv7vAgqyEtmenqPfuNAMdKD+KAePtOr4//ff16MLY//3KZbmpdHU1sX60jo6ux38ZvkObv6/HSzOSeGlH5xMZpJ/V/FyJaPyASilioDBLljzgN1KqVJr22eBCwH3JmoxBDRTUqJRycnUp6ST6EIBOK7+7x9WQFQUnHGGy45vGD2LspOJCgvmhS3l/H31HkGHOmcAAAvESURBVD7YXcf3Fk/ml2dPI9jU1zgOTziBxwEHer0vB/qNyxORa4BrACZOHDiUy2Doj55Z2p+k53CaC2sEry+tJyEqlOyUaD37d+lSiIx02fENoyciNJjTclJYsa2CsOAg7rl4Nl+dM97b3fJJBjUBicjbIrLDyWOo415nkttvbgSl1CNKqQKlVEFKSmBNyjC4lrlZCXyYOAlKS6GuziXH3FCm4/+Dtm2FAweM+cdHuXxBJtPTY3nmmvnm4j8Ag44AlFJLRtlGOTCh1/vxQMUoj2kwDEpBVgL3pGXrN5s3w1lnjep45YePUn64lasWTYIVT5nkbz7MKVOTeeMnp3q7Gz6PJ0IXNgHZIjJJRMKAS4EVHmjXEODMGBfHrnGucwRv+Kz+b5IO/zz5ZDCjVIMfM9ow0C+LSDmwEHhNRN60lmeIyOsASqku4EfAm0AR8LxSaufoum0wDE54SDBTpmZQbp/oEgFYX1pHfFQoue2HYetWM/nL4PeMNgroZeBlJ8srgHN7vX8deH00bRkMI6EgK5HNKVMYt2mTU2fUcFhfVsf8SYkEvWolfzP2f4OfY2avGMY0BZkJbLVnI5WVumLXCDl4pJUD9a3Mn2SZf3JzISfHhT01GDyPEQDDmGZOZgLb0y1H8CjMQD31f09ODtETwMzdv2EMYATAMKaJjwqjY8YsuoOCRyUA60vriIsMJeejtbrOgBEAwxjACIBhzDMzO52SlEzUxlGMAMrqmddj/09JcXmNAYPBGxgBMIx55mYl8LF9Ko7Nm2AY9Zl7qDjSyr66oyycGAuvv66TvwUHu6GnBoNnMQJgGPMUZOrEcMGHD+tZwcNkg5X//4vVn0JDgwn/NIwZjAAYxjwTEiM5MCVfvxmBH2BDaT2xESFMXPuWLjB/5pku7qHB4B2MABjGPCJC/LwTaQ8J0ykhhsn60jrmZSUStGKFvvhHRbmhlwaD5zECYAgITpqSys7USbSv2zCs/aoa2thbd5Rz1SHYv99E/xjGFEYADAHB3KxEtqdlE/zxR7qG7xDpsf+fsvMDEIFly9zVRYPB4xgBMAQE09NtfDo+l5DWo/Dpp0Peb31pHbaIEFLfXwkLFuj6vwbDGMEIgCEgCAkOorvAKls9DEfwhtJ6zortRLZsMeYfw5jDCIAhYEifdwJNYZF0rB+aH6C6sY3S2ha+VP6xXmDCPw1jDCMAhoBh7uQkdqRNpW3dxiFt31P/d/ZHqyA7G6ZNc2f3DAaPYwTAEDCcOFEnhosq+gQ6OgbdfkNZPWm0E/Phan33L6aguGFsYQTAEDDEhIdQP302IZ2d8Mkng26/vrSObzXtQjo7jf3fMCYxAmAIKCJP1kncujcM7Ac41NhGaU0LZ5ash6QkXf7RYBhjGAEwBBRT5s6gPjKWhtXrBtxuQ1k9Id1dTNqwSsf+m+RvhjGIEQBDQFEwSU8IGywUdH1pHadW7yKkscGYfwxjFiMAhoAiPS6SsknTiS8rgZaWfrdbX1rHZZUfQXi4Sf5mGLMYATAEHF0nzSFIOVAff+x0fU1TO3sONbNg54ewZAnExHi4hwaDZzACYAg4Ek4/BYAjqz50un5DWR25tfuIrTxgJn8ZxjRGAAwBx4w5uVTYkmla69wRvKG0nnNLrcli55/vwZ4ZDJ5lVAIgIheLyE4RcYhIwQDb7RWRT0Rkq4gMPyG7weBCclJtFI3LIWqbcxPQ+tI6zt+3Wdf9TU/3cO8MBs8x2hHADuArwOohbPsFpdQJSql+hcJg8ARBQcLhvNkkV+6DI0eOW1fb3E7Dnn1MLis05h/DmGdUAqCUKlJK7XJVZwwGTxG6YB4AzX3MQBvL6lmyxzL/mPBPwxjHUz4ABawUkS0ics1AG4rINSKyWUQ219TUeKh7hkAjY8mpAFS9+8Fxy9eX1nH2ng2oyZMhL88bXTMYPMagAiAib4vIDieP4dwenaKUOgk4B/ihiCzub0Ol1CNKqQKlVEFKSsowmjAYhs6M/Cz2JmTQvfH4zKBbiw6wcO825MILTfI3w5gnZLANlFJLRtuIUqrCej4kIi8D8xia38BgcAuRYcHsn5JHXuH2z5bVNbeTsWENoV2dxv5vCAjcbgISkWgRsfW8BpainccGg1dpn30iyYeraT9YAWj7/5m7N9AVnwCLFnm5dwaD+xltGOiXRaQcWAi8JiJvWsszROR1azM7sFZEtgEbgdeUUv8ZTbsGgyuIXawzfB54cxUAG0uqOWPPJoKWnQchgw6ODQa/Z1S/cqXUy8DLTpZXAOdar0uB2aNpx2BwB1POWky3BHFk9Ydw5WU0vbeG+NYmE/1jCBjMTGBDwJJsT2SfPZPwjz+ivqWD3A3v0h0aCmed5e2uGQwewQiAIaCpnTaLcXt2smFPLWeWbKD55MVgs3m7WwaDRzACYAhogufNI7GlgR1PPEfWkUqiL/6Kt7tkMHgMIwCGgMa+REf7nPXcfwMQ8iVj/zcEDkYADAHNuFPn0Rkcwqyq3VTnzoBx47zdJYPBYxgBMAQ0EhHBwYnZAHSeZ1I/GwILIwCGgMcxRyeoTf3GxV7uicHgWcxsF0PAM/m3v4QZUwg78QRvd8Vg8ChGAAyGGTP0w2AIMIwJyGD4/3buLkSqOg7j+PdBi9IKi14oV9JALJHKkLCELjLBSrTLokKoy14sglK6j6CIgqIIK4XECDOSoHKxoJuKysI0K6UX3bI0ohfqwqSniznCoq6cGZf975nzfGCZOYdd+O4wM79z/md3IloqAyAioqUyACIiWioDICKipTIAIiJaKgMgIqKlMgAiIloqAyAioqVku3TDiCQdAH7o8cfPBn4dxZyx1NT2pnZD2ktJ++i70PY5db5xXA+AEyHpE9vzSnf0oqntTe2GtJeS9rKyBBQR0VIZABERLdXPA+D50gEnoKntTe2GtJeS9oL69hpAREQcXz+fAURExHFkAEREtFTfDQBJiyV9LWm3pJWle+qSNE3Se5J2StohaUXppm5JmiDpM0lvlm7phqQpkjZI+qp6/K8q3VSXpPur58t2SeslnVK6aSSSXpS0X9L2YfvOkjQoaVd1e2bJxmMZofux6vmyTdLrkqaUbOxVXw0ASROAZ4DrgdnALZJml62q7RDwgO1LgPnAXQ1qP2wFsLN0RA+eAt62fTFwGQ35HSRNBe4F5tmeA0wAbi5bdVxrgMVH7FsJbLE9E9hSbY83azi6exCYY/tS4Btg1VhHjYa+GgDAlcBu29/aPgi8Aiwr3FSL7X22t1b3/6LzJjS1bFV9kgaAG4HVpVu6IekM4BrgBQDbB23/XraqKxOBUyVNBCYBPxXuGZHt94Hfjti9DFhb3V8L3DSmUTUcq9v2ZtuHqs0PgYExDxsF/TYApgJ7h20P0aA30cMkTQfmAh+VLenKk8CDwH+lQ7p0EXAAeKlavlotaXLpqDps/wg8DuwB9gF/2N5ctqpr59neB52DIODcwj29uAN4q3REL/ptAOgY+xr1d66STgNeA+6z/WfpnjokLQH22/60dEsPJgJXAM/angv8zfhchjhKtV6+DJgBXABMlnRb2ap2kfQwneXbdaVbetFvA2AImDZse4BxfEp8JEkn0XnzX2d7Y+meLiwAlkr6ns6y27WSXi6bVNsQMGT78NnWBjoDoQmuA76zfcD2v8BG4OrCTd36RdL5ANXt/sI9tUlaDiwBbnVD/6Gq3wbAx8BMSTMknUzngtimwk21SBKddeidtp8o3dMN26tsD9ieTucxf9d2I45Ebf8M7JU0q9q1EPiyYFI39gDzJU2qnj8LacgF7GE2Acur+8uBNwq21CZpMfAQsNT2P6V7etVXA6C6KHM38A6dF8KrtneUraptAXA7naPnz6uvG0pHtcQ9wDpJ24DLgUcK99RSnbVsALYCX9B5PY/bjyeQtB74AJglaUjSncCjwCJJu4BF1fa4MkL308DpwGD1Wn2uaGSP8lEQEREt1VdnABERUV8GQERES2UARES0VAZARERLZQBERLRUBkBEREtlAEREtNT/S7fzjEpJfs4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Y_dict[EPOCHS - 1])\n",
    "plt.plot(y_hat_dict[EPOCHS - 1] * 10, 'r')\n",
    "plt.title('Rescaled out of sample performance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(in_sample_Y_dict[EPOCHS - 1])\n",
    "plt.plot(in_sample_y_hat_dict[EPOCHS - 1] * 10, 'r')\n",
    "plt.title('Rescaled in sample performance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7eff343fc470>]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Y_dict[EPOCHS - 1], y_hat_dict[EPOCHS - 1] * 10, linestyle=\"\", marker=\"o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.4510042465998294, 0.1055373665178873)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pearsonr(Y_dict[EPOCHS - 1], y_hat_dict[EPOCHS - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.14725274725274726, pvalue=0.6154187624847252)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spearmanr(Y_dict[EPOCHS - 1], y_hat_dict[EPOCHS - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63,)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in_sample_Y_dict[EPOCHS-1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
